摘要： 提出了一种新的多目标粒子群优化(MOPSO)算法，该算法采用自适应网格方法来估计非劣解集中粒子的密度信息、平衡全局和局部搜索能力的Pareto最优解的搜索机制、删除品质差的多余粒子的Archive集的修剪技术。通过对三峡梯级多目标优化调度问题的计算，表明该算法是求解大规模复杂多目标优化问题的一种有效手段。

关键词: 多目标优化, 粒子群优化算法, 三峡梯级

Abstract: A new multi-objective particle swarm optimization(MOPSO) is proposed. The proposed algorithms employs three techniques: adaptive grid algorithms, which can obtain the valid density value of particles in Archive set; Pareto optimal solution searching algorithm, which can equalize the ability of global and local searching; Archive pruning techniques, which can remove inferior particles in Archive set to fix the size of Archive set. The algorithm is applied to solve multi-objective optimal regulation of Three Gorges. The simulation performance indicates the effectiveness of the presented algorithm with regard to solving the large scale complex multi-objective optimization problem.

Key words: multi-objective optimization, particle swarm optimization, Three Gorges cascade

中图分类号: 

• TP391